Sound is a longitudinal wave phenomenon. Interference between waves has been known and observed since the time of Sir Isaac Newton. Active suppression of noise has been a goal for many decades. The potential applications of active noise suppression are many, ranging from aircraft cabins to automobile interiors to medical equipment (e.g., dentist drills, operating rooms, etc.) to buildings to household machinery such as vacuum cleaners, lawn mowers, snow blowers, washers, dryers, etc. The concept of wave dynamics and the ability of electronic amplifiers and moving coil speakers to generate accurate sound waves led to techniques used in the 1930s to reduce noise for a harmonic acoustic disturbance. The next important step was taken in the 1950s with the experimental demonstration of a very high-gain static feedback acoustic noise reduction scheme. In today's terminology, it is called a collocated control scheme with its inherent robustness properties. This basic collocated high-gain static feedback scheme is now commercially used for industrial ear protection devices.
In the 1970s, the development of acoustic noise control took a slightly different turn. Papers such as those by Jessel & Mangiante, and Swinbanks addressed the problem of acoustic noise control in ducts. The target application discussed in these papers was the reduction of noise in HVAC systems. The main idea was to measure the noise disturbance using a microphone located at an upstream location and then to feedforward this disturbance via a controller to a speaker located somewhere at the downstream location. The research showed that a proper adjustment of the controller gains would arrest the propagation of the disturbance downstream from the controlling speaker. This control technique, referred to as the feedforward control, led to several years of active noise cancellation research and development using the feedforward approach.
For several years, feedforward methods were very popular. It was argued that since feedforward scheme doesn't alter system dynamics, there was no risk of destabilizing the system (or making it any worse). These schemes required (or otherwise assumed) that the disturbance measuring microphone (located upstream) does not sense the output from the control speaker (located downstream). If it did, then it would be a feedback scenario with a danger of closed-loop instabilities. This meant that the schemes could be successfully implemented only with unidirectional microphones and speakers. In general, these conditions are very stringent. Nevertheless, researchers came up with ingenious arrangements and obtained impressive results. Most well-known are the experiments based on feedforward control by Ross and Roure wherein the controller was designed to perfectly cancel the acoustic noise. These experiments involve exact knowledge of several system transfer functions, i.e., the design needs to calculate differences of transfer functions and invert them leading to high sensitivity of the controllers to uncertainty in the system model. Owing to high sensitivity, the model information has to be obtained experimentally. This remedy, however, did not alleviate the problems as the controllers were very sensitive to the implementation and finite digit arithmetics and had to be implemented with on-line adaptation schemes. In spite of these hurdles, their success was a big inspiration for much of the work in active control of acoustic noise.
However, one important drawback of the prior feedforward strategies that are used is the necessity of an on-line adaptation, not due to the nature of the system (acoustic duct) but due to the nature of the controller itself. Adaptive control comes at a price. For large bandwidth systems such as a 3-D acoustic duct, support of a fast digital signal processor with significant memory is essential for the feedforward techniques to work. Therefore, the implementation of feedforward controllers really got boosted only after the arrival of cheap, high performance DSP (digital signal processor) chips. Another difficulty with feedforward schemes is to ensure that the adaptive control algorithm will always converge. Most algorithms guarantee convergence with known model, known controller order, and known persistency of excitation but, unfortunately, such ideal conditions are very hard to meet. As a result, feedforward methods for noise control are generally effective only at specific locations, usually downstream of the disturbance, and at specific disturbance frequencies. The need to use high performance DSPs for the computationally intensive on-line adaption schemes results in an expensive solution and the lack of robustness can make the noise worse in the event of small deviations.
Since the late nineteen eighties, active noise control research took a multifaceted approach. During this time, research efforts extended to a multitude of problems, including such items as investigation of structure-borne noise, acoustic radiation from structures, reduction of noise in ducts and enclosures, and acoustic-structure interaction. Feedforward adaptive algorithms work well when a signal strongly correlated with the noise source is available. Most of the algorithms converge nicely if the dynamics of the path between the control actuator and the sensor is known. Although some earlier successful solutions to structure-borne sound used multiple microphones and loudspeakers, subsequent studies showed that loudspeakers alone are not very efficient in controlling structure-borne sound. This observation led researchers to invent new actuators that directly act on the structure itself. It was found that an effective way to reduce the radiated acoustic energy from the structure is to control the structure so as to minimize the radiated acoustic power. Although a majority of the research during the 1980's and 1990's still used feedforward adaptive algorithms, a large number of researchers were also engaged in developing and using feedback techniques.
The research in the nineteen nineties especially involved fundamentally new approaches to active noise control—a feedback control instead of traditional feedforward methods. A significant effort was also expended looking at specific problems (e.g., active noise control for aircraft cabins, control of structure-borne noise, and a few others). In spite of difficulties, a number of approaches have succeeded in feedback control of reverberant enclosures but they are restricted primarily to the low-frequency bands. Additionally, much of the experimental work was restricted to small size cavities. Nevertheless, the success stories of feedback methods in nineties gave boost to the researchers for further exploration of feedback concept for active noise control.
The major hurdles in successful implementation of feedforward methods included availability of a reference signal having strong correlation to the noise source, necessity of online adaptation, and lack of controller robustness. For example, for structure-borne sound in aircraft it is difficult to obtain a reference signal which is strongly correlated with the noise producing mechanism. This makes feedforward control very ineffective. The advances in robust control methodologies coupled with knowledge gained in feedforward control schemes made it possible to develop closed-loop stable feedback control methods. Moreover, feedback methods have natural robustness compared to feedforward methods which is inherent in the architecture. A comparison of LQG (linear-quadratic-Gaussian), rate feedback, and filtered-x LMS (least mean squares) algorithm, presented for SISO (single output, single input) feedback control of structure-acoustic dynamics, showed that there is a definite rationale to work with feedback methods.
However, designing controllers for acoustic systems is very challenging since there is no natural roll-off at high frequencies and the systems are modally very rich. In the mid to late nineteen nineties, noteworthy research demonstrating effectiveness of feedback control methodologies emerged. For example, a PDE-based (partial differential equation-based) feedback control design was employed for controlling acoustic noise in 3-D enclosure using piezo-actuated structures. The results were only numerical and the noise was only tonal.
Although the feedback control methodology has great potential, it has several obstacles to overcome. One problem with many feedback control techniques used is that the noise attenuation can be guaranteed only locally (i.e., only at the sensor locations) and not uniformly in space and/or frequency. In fact, the noise levels can go high at some other locations although there is attenuation at the sensor locations. This essentially is the result of redistribution of acoustic energy in space. It is also possible to have worsening of noise levels at the same location due to redistribution of acoustic energy in frequency, commonly referred to as the waterbed effect.